Disease-free survival as a surrogate endpoint for overall survival in adjuvant trials of pancreatic cancer: A meta-analysis of 20 randomized controlled trials

Nie et al. BMC Cancer
(2020) 20:421
/>
RESEARCH ARTICLE

Open Access

Disease-free survival as a surrogate
endpoint for overall survival in adjuvant
trials of pancreatic cancer: a meta-analysis
of 20 randomized controlled trials
Run-Cong Nie1†, Xue-Bin Zou2†, Shu-Qiang Yuan1†, Ying-Bo Chen1†, Shi Chen3, Yong-Ming Chen1,
Guo-Ming Chen1, Xiao-Jiang Chen1, Tian-Qi Luo1, Shu-Man Li4, Jin-Ling Duan4, Yun Wang5*† and Yuan-Fang Li1*†

Abstract
Background: We aimed to assess whether disease-free survival (DFS) could serve as a reliable surrogate endpoint
for overall survival (OS) in adjuvant trials of pancreatic cancer.
Methods: We systematically reviewed adjuvant randomized trials for non-metastatic pancreatic cancer after curative
resection that reported a hazard ratio (HR) for DFS and OS. We assessed the correlation between treatment effect
(HR) on DFS and OS, weighted by sample size or precision of hazard ratio estimate, assuming fixed and random
effects, and calculated the surrogate threshold effect (STE). We also performed sensitivity analyses and a leave-oneout cross validation approach to evaluate the robustness of our findings.
Results: After screening 450 relevant articles, we identified a total of 20 qualifying trails comprising 5170 patients
for quantitative analysis. We noted a strong correlation between the treatment effects for DFS and OS, with
coefficient of determination of 0.82 in the random effect model, 0.82 in the fixed effect model, and 0.80 in the
sample size weighting; the robustness of this finding was further verified by the leave-one-out cross-validation
approach. Sensitivity analyses with restriction to phase 3 trials, large trials, trials with mature follow-up periods, and
trials with adjuvant therapy versus adjuvant therapy strengthened the correlation (0.75 to 0.88) between DFS and
OS. The STE was 0.96 for DFS.
Conclusions: Therefore, DFS could be regarded as a surrogate endpoint for OS in adjuvant trials of pancreatic cancer.
In future similar adjuvant trials, a hazard ratio for DFS of 0.96 or less would predict a treatment impact on OS.
Keywords: Pancreatic cancer, Disease-free survival, Overall survival, Surrogate

* Correspondence: ;
†
Run-Cong Nie, Xue-Bin Zou, Shu-Qiang Yuan, Ying-Bo Chen are contributed
equally to this study.
†
Yun Wang and Yuan-Fang Li are co-senior authors.
5
Department of Hematologic Oncology, Sun Yat-sen University Cancer
Center, State Key Laboratory of Oncology in South China, Collaborative
Innovation Center for Cancer Medicine, No. 651 Dongfeng Eastern Road,
Guangzhou 510060, Guangdong, China
1
Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State
Key Laboratory of Oncology in South China, Collaborative Innovation Center
for Cancer Medicine, Guangzhou, China
Full list of author information is available at the end of the article
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Nie et al. BMC Cancer

(2020) 20:421

Background
Pancreatic cancer is one of the few malignant tumors
with increasing incidence and mortality in both sexes
[1], and it is predicted to become the third leading cause
of death in the European Union in 2020 [2]. Fewer than
20% of pancreatic cancer patients present at a localized,
resectable stage at their first visit, and curative resection
remains the only chance of cure for these patients. Progress in surgical techniques in recent years has likely minimized postoperative complications, which is regarded as
an important factor in long-term survival [3, 4]. However,
in the absence of adjuvant therapy, approximately 90% of
patients suffered from distant or local relapse within 5
years after curative resection, and curative resection alone
only yields a 5-year overall survival (OS) of approximately
8 to 13% [5–7]. Thus, valid adjuvant therapies are required
to reduce this risk.
Several effective therapeutic strategies have been demonstrated to be effective for resectable pancreatic cancer
[5–12], among which adjuvant chemotherapy can significantly reduce the risk of relapse and improve the survival of pancreatic cancer after curative resection [5–10].
To date, adjuvant gemcitabine and S− 1 remains the first
recommendation for non-Asian and Asian patients after
resection, respectively. However, the objective response
rate of single-agent chemotherapy in the metastatic stage
was reported to be low, in the range of 7 to 21% [13–15].

Fig. 1 Study flow diagram of the included studies in this meta-analysis

Page 2 of 10

The landmark CONKO-001 (Charité Onkologie 001)
study showed that 133 of 179 patients (74.3%) suffered

from local relapse (25.3%) or distant metastasis (49.0%)
after adjuvant gemcitabine treatment [16]. Therefore, clinicians are exploring whether more intensive therapeutic
strategies, including combination regimens [17–19], adjuvant chemoradiotherapy [5, 10, 20, 21] and adjuvant immunotherapies [22–24], could enhance the therapeutic
efficacy and translate to a survival benefit. For example,
the PRODIGE 24/CCTG PA.6 trial further demonstrated
that modified FOLFIRINOX regimen could lead to statistically prolonged RFS and OS than gemcitabine for patients with resected pancreatic cancer [19].
The gold standard endpoint in adjuvant trials of pancreatic cancer is OS, which has the advantage of being
simple and reliable to measure, straightforward to interpret, and clinically useful. However, this endpoint has its
disadvantages: it requires many patients and lengthy
follow-up duration to detect statistically significant differences. In addition, its estimates are potentially diluted
by non-cancer deaths and subsequent therapies after recurrence. Therefore, reliable endpoints that could be
used as surrogates for OS in pancreatic cancer could
shorten the follow-up period and reduce the cost of drug
development. Among them, disease-free survival (DFS)
is the reasonable potential surrogate endpoint for OS in
the adjuvant setting of pancreatic cancer. Several meta-


2006–2008
2010–2015

2006

2007

2008

2008

2009

2010

2010

2012

2013

2015

2016

2017

2017

2018

2018

2018

Kosuge et al. [34]

Smeenk et al. [20]

Morak et al. [35]

Yoshitomi et al. [17]

Ueno et al. [6]

Neoptolemos et al. [36]

Van Laethem et al. [21]

Schmidt et al .[22]c

Oettle et al .[7]d

Shimoda et al. [37]

Uesaka et al .[38]c

Neoptolemos et al. [39]

Sinn et al. [18]

Reni et al. [24]

Berlin et al. [23]

Conroy et al. [19]

1986–1992

III

II

II

III

III

III

II

III

III

II

III

III

II

III

III

III

III

III

III

III

Type of study

Stage

R0/1

Stage I–II

R0/1

R0

R0/1

Stage I-III

R0/1

T1-4N0–1 M0

R0/1

R0

R0/1

R0/1

R0/1

Stage I-III

T1-3N0–1aM0

R0

R0/1

Stage II-III

Stage III

R0

Treatment arms

AC vs. AC

AC vs. AC

CRT vs. CRT

AC vs. AC

AC vs. AC

AC vs. AC

AC vs. AC

AC vs. observation

CRT + IFN -2b vs. CRT/AC

CRT vs. AC

AC vs. AC

AC vs. observation

AC vs. AC

CAI/RT vs. observation

CRT vs. observation

AC vs. observation

CRT vs. AC vs. observation

AC vs. observation

CIT vs. AC vs. observation

CRT vs. observation

Number of patients

493

56

130

436

730

377

57

384

110

90

1088

118

99

120

218

89

289

158

128

43

DFS

DFS

Toxicity

DFS

OS

OS

DFS

DFS

OS

Treatment completion

OS

OS

DFS

OS

OS

OS

2-year OS rate

OS

OS

OS

Primary endpoint

Median follow-up (months)

33.6

55.4

NR

54.0

43.2

82.3

NR

136

45.9

33.3

34.2b

60.4b

21.0

17.0

140.4

44.8

47.0

60.0

NR

66

OS overall survival, DFS disease-free survival, CRT chemoradiotherapy, AC adjuvant chemotherapy, RT radiation therapy, CIT chemoimmunotherapy, CAI celiac artery infusion, NR not reported
a
This trial was designed as a two-by-two factorial design to test two comparisons: chemoradiotherapy, and chemotherapy. Patients were randomly assigned to chemoradiotherapy-alone group (n = 73), chemotherapyalone group (n = 75), both chemoradiotherapy and chemotherapy group (n = 72), and observation group (n = 69)
b
Follow-up for the living patients
c
These trials were analyzed by per-protocol population
d
The long-term outcomes of CONKO-001 trial

2012–2016

2008–2013

2008–2014

2007–2010

2008–2012

1998–2004

2004–2007

2004–1007

2000–2007

2002–2005

2002–2005

2000–2007

1987–1995

1992–2000

1994–2000

2002

1993–2000

2004

2002

Lygidakis et al. [8]

1974–1982

Trial conduct period

Neoptolemos et al. [10]a

1985

Kalser et al. [5]

Takada et al. [9]

Final pub year

Studies

Table 1 Characteristics of the included studies

Nie et al. BMC Cancer
(2020) 20:421
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Nie et al. BMC Cancer

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Page 4 of 10

analyses have revealed that DFS is validated as a surrogate for OS in lung cancer [25], gastric cancer [26] and
colorectal cancer [27]. Although Petrelli et al. reported

that DFS cannot represent a reliable surrogate endpoint
for OS in adjuvant trials of pancreatic cancer [28], the
number of included trials in that study was comparatively small (12 trials); additionally, among the 12 trials,
one was the adjuvant trial of periampullary adenocarcinoma (the ESPAC-3 periampullary cancer randomized
trial) rather than pancreatic cancer [29], which would
confound the results.
Therefore, with the accumulated evidence of 20 randomized controlled trials, we performed a rigid metaanalysis to evaluate whether DFS could be used as a
surrogate endpoint to measure the effect of the adjuvant
therapy of pancreatic cancer.

Methods
Search strategy and data collection

In December 2018, we searched Medline and Embase
systematically using the key words “pancreatic neoplasm”, “chemotherapy”, “radiotherapy”, and “chemoradiotherapy”, limited to “clinical trial”, “controlled clinical
trial” or “randomized controlled trial”. We also search
the ClinicalTrials. Gov and Cochrane Library databases,
and manually searched the references of the included trials and abstracts of two conference proceedings (the
2019 American Society of Clinical Oncology [ASCO] annual meeting and the European Society for Medical Oncology [ESMO] 2018 congress) to retrieve additional
studies.
Inclusion criteria were randomized controlled trials of
adjuvant treatment for non-metastatic pancreatic cancer

Table 2 Disease-free survival and overall survival estimate for the included trials
Study

Number of patients

Disease-free survival

Overall survival

Experimental arm

Control arm

Hazard ratio

95% CI

Hazard ratio

95% CI

21

22

0.45

0.25–0.83

0.51

0.28–0.94

CIT vs. AC

43

45

0.63

0.42–0.96

0.61

0.40–0.93

CIT vs. observation

43

40

0.49

0.32–0.75

0.60

0.39–0.92

AC vs. observation

45

40

0.57

0.37–0.87

0.65

0.42–1.00

Kalser et al. [5]
Lygidakis et al. [8]

Takada et al. [9]

a

81

77

0.97

0.93–1.30

0.86

0.63–1.18

CRT vs. no CRT

145

144

1.27

1.01–1.60

1.28

0.99–1.66

AC vs. no AC

147

142

0.76

0.60–0.96

0.71

0.55–0.92

Kosuge et al. [34]

45

44

1.03

0.68–1.56

1.18

0.78–1.79

Smeenk et al. [20]

110

108

0.94

0.70–1.26

0.91

0.68–1.23

Neoptolemos et al .[10]b

Morak et al. [35]

59

61

0.64

0.45–0.92

0.81

0.57–1.16

Yoshitomi et al. [17]

50

49

1.09

0.74–1.62

1.24

0.84–1.84

Ueno et al. [6]

58

60

0.60

0.40–0.89

0.77

0.51–1.14

Neoptolemos et al. [36]

537

551

0.96

0.84–1.10

0.94

0.81–1.08

Van Laethem et al. [21]

45

45

1.00

0.66–1.51

1.01

0.67–1.53

Schmidt et al. [22]c

53

57

0.91

0.63–1.31

0.88

0.61–1.27

Oettle et al. [7]d

179

175

0.55

0.44–0.69

0.76

0.61–0.95

Shimoda et al. [37]

29

28

0.67

0.40–1.11

0.70

0.36–1.36

Uesaka et al. [38]

c

187

190

0.60

0.47–0.76

0.57

0.44–0.72

Neoptolemos et al. [39]

364

366

0.86

0.73–1.02

0.82

0.68–0.98

Sinn et al. [18]

219

217

0.94

0.76–1.15

0.93

0.70–1.23

Reni et al. [24]

67

63

1.12

0.78–1.61

1.06

0.73–1.55

Berlin et al. [23]

30

26

0.53

0.30–0.96

0.86

0.41–1.81

Conroy et al. [19]

247

246

0.58

0.46–0.73

0.64

0.48–0.86

a

Hazard ratio for 5-year disease-free survival
This trial was designed as a two-by-two factorial design to test two comparisons: chemoradiotherapy, and chemotherapy. Patients were randomly assigned to
chemoradiotherapy-alone group (n = 73), chemotherapy-alone group (n = 75), both chemoradiotherapy and chemotherapy group (n = 72), and observation
group (n = 69)
c
These trials were analyzed by per-protocol population
d
The long-term outcomes of CONKO-001 trial
b


Nie et al. BMC Cancer

(2020) 20:421

after curative resection, reporting hazard ratio (HR) for
OS and DFS in full-text publication. We excluded reviews, abstracts, case reports, studies that were not published as full-text articles and studies with cohorts of
less than 50 patients. For each trial, the following data
were collected by two independent investigators (RCN
and SQY): OS and DFS results, final publication year,
trial conduct period, type of study (phase II or III), staging information, treatment arms, number of patients,
primary endpoint, and median follow-up time.
Statistical analysis

This analysis is at the trial level throughout, with no individual patient-level data being incorporated. We computed
the correlation between the treatment effect (HR) on DFS
and OS through a linear regression model [27]. To interpret
the differences between studies regarding study size and
precision of HR estimates, we weighted the analysis proportionally to the study sample size or to the precision of the
observed treatment effects. Hence, we applied three weighting strategies (sample size, fixed effect, and random effect)
as the weighting strategies [30]. While the fixed effect
meta-analysis is based on the presumption that a common
treatment effect exists among every trial and uses the estimated inverse variance as weights, the random effect metaanalysis permits treatment effect discrepancy from trial to
trial and merges the potential among-trial variation of effects into the weights. According to A’ Hern et al. [31], we
down-weighted the sample size if trials reported more than
two treatment arms.

Page 5 of 10

We calculated the weighted coefficient of determination
(R2) to quantify the variation explained by the surrogate
endpoints, with R2 value higher than 0.75 as a strong correlation, higher than 0.5 as good, higher than 0.25 as moderate, and equal to or lower than 0.25 as poor. We
performed several sensitivity analyses that restricted the
analyses to phase 3 trials, large trials (included patients
≥200), trials with mature follow-up periods (median

follow-up ≥24 months), trials with adjuvant therapy versus
observation, and trials with adjuvant therapy versus adjuvant therapy to verify the robustness of our findings. We
also calculated the surrogate threshold effect (STE), which
was defined as the minimum treatment effect on the surrogate necessary to predict an OS benefit [32]. The upper
limit of the confidence interval for the estimated surrogate
treatment effect should fall below the STE to predict a
non-zero effect on OS. For each meta-analysis, we applied
an internal validation through leave-one-out analysis to
evaluate the prediction accuracy of the surrogate model
[33]. Each trial was left out once, and the surrogate model
was built with other trials. This model was then re-applied
to the left-out trial, and a 95% prediction interval was calculated to compare the predicted and observed treatment
effect on OS. We used R version 3.4.0 for all statistical
analyses ().

Results
After the systematic literature review, we identified 20
qualifying trials (5 phase 2 trials and 15 phase 3 trials)
comprising 5170 patients for final analysis (Fig. 1,

Fig. 2 Correlation between treatment effects on DFS and OS. Each trial is represented by a circle, with the size of the circle being proportional to
the sample size. The blue line represents the 95% prediction limit of the regression line (red line). STE = 0.96; OS, overall survival; DFS, disease-free
survival; STE, surrogate threshold effect; HR, hazard ratio


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Page 6 of 10

Table 1) [5–10, 17–24, 34–39]. The median follow-up
period of the included trials varied from 17.0 months to
104.4 months. The ESPAC-1 trial (European Study
Group for Pancreatic Cancer-1) [10] was designed as a
two-by-two factorial design to evaluate the role of adjuvant
chemoradiotherapy and chemotherapy independently, with
75 patients randomly divided into the chemotherapy group,
73 patients in the chemoradiotherapy group, 72 patients in
the chemoradiotherapy and chemotherapy group, and 69
patients in the observation group. Neoptolemos et al. reported the interim result of ESPAC-1 trial in 2001 [40], and
updated the long-term survival outcomes after a median
follow-up of 47.0 months [10]; thus, we included the latter
publication in the present study. The CONKO-001 trial
was also first published in 2007 [16] and was updated in
2013 [7]. Overall, the 20 trials included 23 comparisons for
quantitative analysis, among which nine comparisons reported improvement in OS, and eleven comparisons reported improvement in DFS (Table 2).
We first assessed the degree of association through
sample size weighting strategy, and observed that the

correlation between the treatment effect on DFS and OS
was strong (R2 = 0.80, 95% CI: 0.49 to 0.99) (Fig. 2). Additionally, we noted that permitting difference (random
effect model) and no difference (fixed effect model) between therapy type and treatment effect on DFS and OS
slightly strengthened the degree of association (fixed effect: 0.82, 0.52 to 0.99; random effect: 0.82, 0.52 to 0.99).
We then calculated the STE of 0.96, indicating that a future adjuvant trial would need less than 0.96 for DFS of
the upper limit of the confidence interval to predict with
95% confidence an OS benefit.
Given the potential heterogeneity of the included studies,
we performed several sensitivity analyses (Table 3), and
noted that restriction of the analysis to phase 3 trials would

strengthen the correlation between DFS and OS (0.82 to
0.83). When we restricted the analyses to trials with adjuvant therapy versus observation, the degree of association
between DFS and OS was not strong (0.68 to 0.73) (Fig. 3a).
Nonetheless, we recognized that adjuvant therapy versus
adjuvant therapy rather than observation is now the standard design setting for pancreatic cancer; thus, we then

Table 3 Sensitivity analysis
R2 (95% CI)

P value

0.80 (0.49 to 0.99)

< 0.001

Total population [5–10, 17–24, 34–39]
Sample size

0.96

Fixed effect

0.82 (0.52 to 0.99)

< 0.001

Random effect

0.82 (0.52 to 0.99)

< 0.001

0.82 (0.48 to 0.99)

< 0.001

Phase 3 trials [5–10, 18–20, 22, 34–36, 38, 39]
Sample size

0.96

Fixed effect

0.82 (0.49 to 0.99)

< 0.001

Random effect

0.83 (0.50 to 0.99)

< 0.001

Trials with overall included patients ≥ 200 [7, 10, 18–20, 36, 38, 39]
Sample size

0.85 (0.41 to 0.99)

0.93
< 0.001

Fixed effect

0.86 (0.41 to 0.99)

< 0.001

Random effect

0.87 (0.44 to 0.99)

< 0.001

Trials with median follow-up ≥ 24 months [6, 7, 9, 10, 18–23, 34, 36, 38, 39]
Sample size

0.80 (0.43 to 0.99)

0.95
< 0.001

Fixed effect

0.81 (0.45 to 0.99)

< 0.001

Random effect

0.80 (0.43 to 0.99)

< 0.001

Trials with adjuvant therapy versus observation [5–9, 20, 34, 35]
Sample size

0.68 (0.17 to 0.99)

0.81
0.006

Fixed effect

0.69 (0.18 to 0.99)

0.005

Random effect

0.73 (0.22 to 0.99)

0.003

Trials with adjuvant therapy versus adjuvant therapy [8, 17–19, 21–24, 36, 38, 39]
Sample size

STE

0.90 (0.59 to 0.99)

0.96
< 0.001

Fixed effect

0.93 (0.66 to 0.99)

< 0.001

Random effect

0.89 (0.58 to 0.99)

< 0.001

R2 coefficient of determination, STE surrogate threshold effect


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Fig. 3 Correlation between treatment effects on DFS and OS (related to Table 3) according the sensitivity analysis that restricted to trials with
adjuvant therapy versus observation (a) and trials with adjuvant therapy versus adjuvant therapy (b). Each trial is represented by a circle, with the
size of the circle being proportional to the sample size. The blue line represents the 95% prediction limit of the regression line (red line). OS,
overall survival; DFS, disease-free survival; HR, hazard ratio

restricted the analyses to trials with adjuvant therapy versus

adjuvant therapy, and observed a very strong correlation
between DFS and OS (0.89 to 0.93). Other sensitivity analyses that restricted the analyses to large trials and trials
with mature follow-up periods also exhibited strong correlations between DFS and OS (0.80 to 0.87) (Fig. 3b).
Finally, we performed a leave-one-out cross validation
approach to assess the accuracy of DFS in predicting
OS. We noted that the observed HR for OS fell between
the limits of the 95% prediction intervals in 22 of 23
comparisons, indicating that the treatment effect on DFS
is a reliable predictor of OS (Fig. 4).

Discussion
The point at which a potential surrogate endpoint could
be theoretically validated has been seriously discussed

[41]. The correlation approach has been widely adopted
to validate the efficiency of a surrogate endpoint in locally advanced lung cancer [25], gastric cancer [26, 42]
and colorectal cancer [27]. In the present study, we included a total of 20 high quality adjuvant randomized
controlled trials to evaluate the surrogacy of DFS for OS
in pancreatic cancer. Our finding demonstrated that the
correlation between DFS and OS was strong (0.80 to
0.82), irrespective of the applied weighting strategies.
Sensitivity analyses that were restricted to phase 3 trials,
large trials, trials with mature follow-up periods, and trials with adjuvant therapy versus adjuvant therapy also
yielded strong or very strong correlations (0.80 to 0.93)
between DFS and OS. Therefore, we proposed the use of
DFS as the surrogate endpoint for OS in adjuvant trials
of pancreatic cancer.


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Page 8 of 10

Fig. 4 Leave-one-out cross-validation analysis of the prediction of OS by treatment effect on DFS: observed HR for OS for left-out trial vs.
predicted HR for OS and 95% prediction interval for predicted HR for OS. To assess model accuracy, a leave-one-out cross-validation strategy was
used: each unit of analysis was left out once, and the linear model was then constructed from scratch using the remaining data [33]. This model
was then re-applied to the left-out study in order to compare the predicted and observed treatment effect on OS. Based on the linear regression
models, a 95% prediction interval was calculated compare the predicted and observed treatment effect on OS. OS, overall survival; DFS, diseasefree survival; HR, hazard ratio

Although the recent advance in adjuvant chemotherapy have translated into substantial survival benefit for
pancreatic cancer, a large number of these treated patients still suffered from relapse or metastasis; thus, new
therapeutic strategies are urgently needed. Clinicians are
now evaluating the therapeutic effect of more intensive
adjuvant chemotherapy, adjuvant targeted therapy and
immunotherapy in pancreatic cancer after curative resection. It is well recognized that OS is the standard
endpoint for clinical trials; however, using the endpoint
of OS to perform the phase 3 trials is time consuming,
thus postponing the new therapy strategies in clinical
application. Therefore, we urgently need reliable surrogate endpoints for OS in adjuvant trials of pancreatic
cancer, among which DFS is the most reasonable surrogate endpoint, and it has been set as the primary endpoint in several phase 3 trials [7, 17–19, 23, 37]. A
previous meta-analysis reported that the correlation between DFS and OS was not strong enough to support
the DFS as the reliable surrogate endpoint for OS in adjuvant trials of pancreatic cancer [28]; nonetheless, they
only included a total of 12 trials, among which one trial
was adjuvant setting for periampullary cancer rather
than pancreatic cancer [29]. Therefore, in the present
meta-analysis, we applied more rigorous criteria through
three weighting strategies to address this urgent issue.
Our findings revealed that the degree of association between DFS and OS was strong, which was further

verified through extensive sensitivity analyses and a
leave-one-out analysis validation approach. We believe
that the robust correlation between DFS and OS in adjuvant therapy of pancreatic cancer is mainly attributable
to the fact that pancreatic cancer is an aggressive tumor
and that the subsequent lines of therapy are limited if
patients develop relapse or metastasis.
Given the fact that adjuvant chemotherapy has showed
superior survival outcome to observation for pancreatic
cancer, adjuvant chemotherapy including gemcitabinebased or S-1-based regimens rather than observation
would be set as the control arm in adjuvant trials. Interesting, we found that the correlation between DFS and
OS was not strong (0.68 to 0.73) with restriction to trials
with adjuvant therapy versus observation; nonetheless,
we noted a very strong correlation between DFS and OS
when we restricted the analysis to trials with adjuvant
therapy versus adjuvant therapy (0.89 to 0.93). Therefore, in future adjuvant trials of pancreatic cancer, DFS
could be served as the robust surrogate endpoint for OS.
STE is an alternative measure for surrogate endpoint
validation [32]. Using a surrogate endpoint with STE
closer to 1, it would be easier to predict an OS benefit. In
the present meta-analysis, our finding showed that the
STE was 0.96 for DFS, indicating that an adjuvant trial in
pancreatic cancer producing a hazard reduction of at least
4% for disease recurrence or death could be expected to
promise a statistically significant reduction in OS.


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There are several limitations that should be noted.
First, the data for our analysis were extracted from trial
level rather than an individual patient; therefore, a potential published bias cannot be excluded. Second, the
included trials spanned nearly three decades, and the ascertainment of DFS was mainly influenced by the image
examination and surveillance interval, thus may have
changed considerably over time and among trials. Third,
long-term follow-up was not available from all trials included in our analysis. Pancreatic cancer is a relatively
aggressive malignancy with severe heterogeneity; thus,
the short follow-up in adjuvant trials will result in fairly
wide confidence intervals of HR about the treatment effects. In the sensitivity analysis, the correlation between
DFS and OS remained strong (R2 = 0.75) when we included trials with median follow-up > 24 months. Third,
the included trials at our analysis comprised a wide
range of therapeutic strategies, which included trials of
adjuvant chemotherapy, radiation therapy, chemoradiotherapy, chemoimmunotherapy and targeted treatment.
Although we performed sensitivity analysis to eliminate
the potential effect of these treatment heterogeneities,
the results of our analysis should be interpreted with
caution. Therefore, we strongly recommended authors
of individual trials to share their data to further verify
the results of our analysis through individual-patient
data.

Conclusions
In conclusion, our analysis suggested that DFS could
serve as a reliable surrogate endpoint for OS in adjuvant
trials of pancreatic cancer. In future similar adjuvant trials,
a hazard ratio for DFS of 0.96 or less would predict a
treatment impact on OS. However, these results should be
further verified by individual-patient data analysis.

Abbreviations
OS: Overall survival; DFS: Disease-free survival; ASCO: American Society of
Clinical Oncology; ESMO: European Society for Medical Oncology; HR: Hazard
ratio; R2: Coefficient of determination; STE: Surrogate threshold effect
Acknowledgments
Not applicable.
Authors’ contributions
Conception, design and data analysis: RCN, XBZ, SQY, YBC, YW, YFL, SC, YMC,
GMC, XJC, TQL, SML and JLD. Interpretation of data: RCN and XBZ. Initial
manuscript writing: RCN, XBZ and SQY. Revision of manuscript: YFL and YW.
Critical lecture and final approval of the manuscript: all authors.
Funding
No funding to declare.
Availability of data and materials
All data generated or analysed during this study are included in this
published article.
Ethics approval and consent to participate
Not applicable.

Page 9 of 10

Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State
Key Laboratory of Oncology in South China, Collaborative Innovation Center
for Cancer Medicine, Guangzhou, China. 2Department of Ultrasound, Sun

Yat-sen University Cancer Center, State Key Laboratory of Oncology in South
China, Collaborative Innovation Center for Cancer Medicine, Guangzhou,
China. 3Department of Gastric Surgery, The 6th Affiliated Hospital, Sun
Yat-sen University, Guangzhou, China. 4Department of Experimental Research
(Cancer Institute), Sun Yat-sen University Cancer Center, State Key Laboratory
of Oncology in South China, Collaborative Innovation Center for Cancer
Medicine, Guangzhou, China. 5Department of Hematologic Oncology, Sun
Yat-sen University Cancer Center, State Key Laboratory of Oncology in South
China, Collaborative Innovation Center for Cancer Medicine, No. 651
Dongfeng Eastern Road, Guangzhou 510060, Guangdong, China.
Received: 8 August 2019 Accepted: 28 April 2020

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